SoroushVahidi/scidocs huggingface dataset:
Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity.
(2015)

IMPORTANCE
Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive.


OBJECTIVE
To investigate network dysfunction in MDD through a meta-analysis of rsFC studies.


DATA SOURCES
Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data.


STUDY SELECTION
Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis.


DATA EXTRACTION AND SYNTHESIS
Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network.


RESULTS
Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions.


CONCLUSIONS AND RELEVANCE
Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.
Greedy Function Approximation : A Gradient Boosting Machine
(2002)

Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additive expansions based on any tting criterion. Speci c algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classi cation. Special enhancements are derived for the particular case where the individual additive components are decision trees, and tools for interpreting such \TreeBoost" models are presented. Gradient boosting of decision trees produces competitive, highly robust, interpretable procedures for regression and classi cation, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire 1996, and Friedman, Hastie, and Tibshirani 1998 are discussed. 1 Function estimation In the function estimation problem one has a system consisting of a random \output" or \response" variable y and a set of random \input" or \explanatory" variables x = fx1; ; xng. Given a \training" sample fyi;xig N 1 of known (y;x){values, the goal is to nd a function F (x) that maps x to y, such that over the joint distribution of all (y;x){values, the expected value of some speci ed loss function (y; F (x)) is minimized F (x) = argmin F (x) Ey;x (y; F (x)) = argmin F (x) Ex [Ey( (y; F (x)) jx] : (1) Frequently employed loss functions (y; F ) include squared{error (y F ) and absolute error jy F j for y 2 R (regression), and negative binomial log{likelihood, log(1 + e 2yF ), when y 2 f 1; 1g (classi cation). A common procedure is to take F (x) to be a member of a parameterized class of functions F (x;P), where P = fP1; P2; g is a set of parameters. In this paper we focus on \additive" expansions of the form
FPGA implementations of fast fourier transforms for real-time signal and image processing
(2003)

Applications based on Fast Fourier Transform (FFT) such as signal and image processing require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. At present, however, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realisation of a High Level framework for the implementation of 1-D and 2-D FFTs for real-time applications. Results show that the parallel implementation of 2-D FFT achieves virtually linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrisable environment based on the developed parallel 2-D FFT architecture is presented as a solution for frequencydomain image filtering application.
Priors for people tracking from small training sets
(2005)

We advocate the use of scaled Gaussian process latent variable models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a low-dimensional embedding of the high-dimensional pose data and a density function that both gives higher probability to points close to training data and provides a nonlinear probabilistic mapping from the low-dimensional latent space to the full-dimensional pose space. The SGPLVM is a natural choice when only small amounts of training data are available. We demonstrate our approach with two distinct motions, golfing and walking. We show that the SGPLVM sufficiently constrains the problem such that tracking can be accomplished with straightforward deterministic optimization.
Three dual polarized 2.4GHz microstrip patch antennas for active antenna and in-band full duplex applications
(2016)

This paper presents the design, implementation and interport isolation performance evaluation of three dual port, dual polarized 2.4GHz microstrip patch antennas. Input matching and interport isolation (both DC and RF) performance of implemented antennas has been compared by measuring the input return losses (Sii, S22) and interport isolation (S12) respectively at 2.4GHz. Two implemented single layer antennas provide around 40dB RF interport isolation while the multilayer antenna has 60dB isolation between transmit and receive ports at centre frequency with DC isolated ports. The multilayer antenna provides more than 55dB interport isolation for antenna's 10 dB input impedance bandwidth of 50MHz.
Evaluating Color Descriptors for Object and Scene Recognition
(2010)

Image category recognition is important to access visual information on the level of objects and scene types. So far, intensity-based descriptors have been widely used for feature extraction at salient points. To increase illumination invariance and discriminative power, color descriptors have been proposed. Because many different descriptors exist, a structured overview is required of color invariant descriptors in the context of image category recognition. Therefore, this paper studies the invariance properties and the distinctiveness of color descriptors (software to compute the color descriptors from this paper is available from http://www.colordescriptors.com) in a structured way. The analytical invariance properties of color descriptors are explored, using a taxonomy based on invariance properties with respect to photometric transformations, and tested experimentally using a data set with known illumination conditions. In addition, the distinctiveness of color descriptors is assessed experimentally using two benchmarks, one from the image domain and one from the video domain. From the theoretical and experimental results, it can be derived that invariance to light intensity changes and light color changes affects category recognition. The results further reveal that, for light intensity shifts, the usefulness of invariance is category-specific. Overall, when choosing a single descriptor and no prior knowledge about the data set and object and scene categories is available, the OpponentSIFT is recommended. Furthermore, a combined set of color descriptors outperforms intensity-based SIFT and improves category recognition by 8 percent on the PASCAL VOC 2007 and by 7 percent on the Mediamill Challenge.
Automatic annotation of human actions in video
(2009)

This paper addresses the problem of automatic temporal annotation of realistic human actions in video using minimal manual supervision. To this end we consider two associated problems: (a) weakly-supervised learning of action models from readily available annotations, and (b) temporal localization of human actions in test videos. To avoid the prohibitive cost of manual annotation for training, we use movie scripts as a means of weak supervision. Scripts, however, provide only implicit, noisy, and imprecise information about the type and location of actions in video. We address this problem with a kernel-based discriminative clustering algorithm that locates actions in the weakly-labeled training data. Using the obtained action samples, we train temporal action detectors and apply them to locate actions in the raw video data. Our experiments demonstrate that the proposed method for weakly-supervised learning of action models leads to significant improvement in action detection. We present detection results for three action classes in four feature length movies with challenging and realistic video data.
Semi-supervised hashing for scalable image retrieval
(2010)

Large scale image search has recently attracted considerable attention due to easy availability of huge amounts of data. Several hashing methods have been proposed to allow approximate but highly efficient search. Unsupervised hashing methods show good performance with metric distances but, in image search, semantic similarity is usually given in terms of labeled pairs of images. There exist supervised hashing methods that can handle such semantic similarity but they are prone to overfitting when labeled data is small or noisy. Moreover, these methods are usually very slow to train. In this work, we propose a semi-supervised hashing method that is formulated as minimizing empirical error on the labeled data while maximizing variance and independence of hash bits over the labeled and unlabeled data. The proposed method can handle both metric as well as semantic similarity. The experimental results on two large datasets (up to one million samples) demonstrate its superior performance over state-of-the-art supervised and unsupervised methods.
Investigating Causal Relations by Econometric Models and Cross-spectral Methods
(2007)

Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/econosoc.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission.
Semantic analysis of soccer video using dynamic Bayesian network
(2006)

Video semantic analysis is formulated based on the low-level image features and the high-level knowledge which is encoded in abstract, nongeometric representations. This paper introduces a semantic analysis system based on Bayesian network (BN) and dynamic Bayesian network (DBN). It is validated in the particular domain of soccer game videos. Based on BN/DBN, it can identify the special events in soccer games such as goal event, corner kick event, penalty kick event, and card event. The video analyzer extracts the low-level evidences, whereas the semantic analyzer uses BN/DBN to interpret the high-level semantics. Different from previous shot-based semantic analysis approaches, the proposed semantic analysis is frame-based for each input frame, it provides the current semantics of the event nodes as well as the hidden nodes. Another contribution is that the BN and DBN are automatically generated by the training process instead of determined by ad hoc. The last contribution is that we introduce a so-called temporal intervening network to improve the accuracy of the semantics output
A Flexible Model-Driven Game Development Approach
(2015)

Game developers are facing an increasing demand for new games every year. Game development tools can be of great help, but require highly specialized professionals. Also, just as any software development effort, game development has some challenges. Model-Driven Game Development (MDGD) is suggested as a means to solve some of these challenges, but with a loss in flexibility. We propose a MDGD approach that combines multiple domain-specific languages (DSLs) with design patterns to provide flexibility and allow generated code to be integrated with manual code. After experimentation, we observed that, with the approach, less experienced developers can create games faster and more easily, and the product of code generation can be customized with manually written code, providing flexibility. However, with MDGD, developers become less familiar with the code, making manual codification more difficult.
Matchmaking in multi-player on-line games: studying user traces to improve the user experience
(2014)

Designing and implementing a quality matchmaking service for Multiplayer Online Games requires an extensive knowledge of the habits, behaviors and expectations of the players. Gathering and analyzing traces of real games offers insight on these matters, but game server providers are very protective of such data in order to deter possible reuse by the competition and to prevent cheating. We circumvented this issue by gathering public data from a League of Legends server (information over more than 28 million game sessions). In this paper, we present our database which is freely available online, and we detail the analysis and conclusions we draw from this data regarding the expected requirements for the matchmaking service.
Knowledge sharing in organisational contexts: a motivation-based perspective
(2010)

Purpose – Facilitating knowledge sharing within organisations is a difficult task: the willingness of individuals to share and integrate their knowledge is one of the central barriers. This paper aims to develop a motivation-based perspective to explore how organisations resolve the social dilemma of knowledge sharing. Design/methodology/approach – The analysis builds on a three-category taxonomy of motivation, adding ‘‘hedonic’’ motivation to the traditional dichotomy of ‘‘extrinsic’’ and ‘‘intrinsic’’ motivation. It uses case studies gleaned from the literature to explore the interactive effects between the different motivators in two different types of knowledge-intensive organisations: professional bureaucracy and operating adhocracy. Findings – Within a professional bureaucracy, the social dilemma of knowledge sharing may be overcome through normative motivation, with provision of hedonic motivation through extrinsic incentives such as training and career progression. In an operating adhocracy where interdependent teamwork is vital, it may be overcome through normative alignment reinforced by intensive socialisation. Extrinsic motivators that align with hedonic motivation may also reinforce the propensity for knowledge sharing. In both organisational types, financial extrinsic incentives do not appear to be relevant on their own, and may ‘‘crowd out’’ other motivators. Research limitations/implications – The cases reported were chosen from the existing literature and, although many were not designed specifically to address motivational issues, suggestive conclusions are drawn. Most of the cases were drawn from organisations rooted in the Anglo-American context and thus care would be needed in generalising the findings to organisations in other contexts. Originality/value – The paper represents the first attempt to apply a three-category taxonomy of motivation to examine knowledge-sharing behaviour in organisations. It highlights the interaction between the different motivators and provides a basis to integrate further the work of social psychologists and socio-economists on incentives and motivation in the context of knowledge sharing.
A versatile FMCW Radar System Simulator for Millimeter-Wave Applications
(2008)

For the successful design of low-cost and high-performance radar systems accurate and efficient system simulation is a key requirement. In this paper we present a new versatile simulation environment for frequency-modulated continuous-wave radar systems. Besides common hardware simulation it covers integrated system simulation and concept analysis from signal synthesis to baseband. It includes a flexible scenario generator, accurate noise modeling, and efficiently delivers simulation data for development and testing of signal processing algorithms. A comparison of simulations and measurement results for an integrated 77-GHz radar prototype shows the capabilities of the simulator on two different scenarios.
Automatic multilabel classification for Indonesian news articles
(2015)

Problem transformation and algorithm adaptation are the two main approaches in machine learning to solve multilabel classification problem. The purpose of this paper is to investigate both approaches in multilabel classification for Indonesian news articles. Since this classification deals with a large number of features, we also employ some feature selection methods to reduce feature dimension. There are four factors as the focuses of this paper, i.e., feature weighting method, feature selection method, multilabel classification approach, and single-label classification algorithm. These factors will be combined to determine the best combination. The experiments show that the best performer for multilabel classification of Indonesian news articles is the combination of TF-IDF feature weighting method, Symmetrical Uncertainty feature selection method, Calibrated Label Ranking - which belongs to problem transformation approach -, and SVM algorithm. This best combination achieves F-measure of 85.13% in 10-fold cross-validation, but the F-measure decreases to 76.73% in testing because of OOV.
Topic Models for Mortality Modeling in Intensive Care Units
(2012)

Mortality prediction is an important problem in the intensive care unit (ICU) because it is helpful for understanding patients’ evolving severity, quality of care, and comparing treatments. Most ICU mortality models primarily consider structured data and physiological waveforms (Le Gall et al., 1993). An important limitation of these structured data approaches is that they miss a lot of vital information captured in providers’ free text notes and reports. In this paper, we propose an approach to mortality prediction that incorporates the information from free text notes using topic modeling.
A non-isolated three-port converter for stand-alone renewable power system
(2012)

A novel non-isolated three-port converter (NI-TPC) is proposed interfacing one PV port, one bidirectional battery port and one load port. Single stage power conversion between any two of the three ports is achieved. The topology is derived by decoupling the bidirectional power flow path of the conventional structure into two unidirectional ones. Two of the three ports can be tightly regulated to achieve maximum power harvesting for PV or charge control for battery, and maintain the load voltage constant at the same time, while the third port is left flexible to compensate the power imbalance of the converter. Operation states are analyzed. The multi-regulator competition control strategy is presented to achieve autonomous and smooth state switching when the PV input power fluctuates. The analysis is verified by the experimental results.
A Framework for Coordinated Surface Operations Planning at Dallas-Fort Worth International Airport
(2007)

An Integer Programming formulation is developed for optimizing surface operations at Dallas-Fort Worth airport, with the goal of assessing the potential benefits of taxi route planning. The model is based on operations in the eastern half of the airport under the most frequently used configuration. The focus is on operational concepts that optimize taxi routes by utilizing different control points on the airport surface. The benefits of two different concepts for optimizing taxiway operations, namely controlled pushback and taxi reroutes are analyzed, for both current data and a projected data set with approximately twice the traffic density. The analysis estimates that: (1) for current traffic densities, controlled pushback would reduce the average departure taxi time by 17% without altering runway schedule conformance, while the benefits of taxi reroutes would be minimal; and (2) for high-density operations, controlled pushback would reduce the average departure taxi time by 18%, while incorporating taxi reroutes would reduce the average arrival taxi time by 14%. Other benefits analyzed for these control strategies include a decrease in the average time spent in runway crossing queues.
Unsupervised Learning of Sequence Representations by Autoencoders
(2018)

Sequence data is challenging for machine learning approaches, because the lengths of the sequences may vary between samples. In this paper, we present an unsupervised learning model for sequence data, called the Integrated Sequence Autoencoder (ISA), to learn a fixed-length vectorial representation by minimizing the reconstruction error. Specifically, we propose to integrate two classical mechanisms for sequence reconstruction which takes into account both the global silhouette information and the local temporal dependencies. Furthermore, we propose a stop feature that serves as a temporal stamp to guide the reconstruction process, which results in a higher-quality representation. The learned representation is able to effectively summarize not only the apparent features, but also the underlying and high-level style information. Take for example a speech sequence sample: our ISA model can not only recognize the spoken text (apparent feature), but can also discriminate the speaker who utters the audio (more high-level style). One promising application of the ISA model is that it can be readily used in the semi-supervised learning scenario, in which a large amount of unlabeled data is leveraged to extract high-quality sequence representations and thus to improve the performance of the subsequent supervised learning tasks on limited labeled data.